What is the role of spin-offs in the entrepreneurial university?
To continue from Pt.1, here is more detailed analysis of what the entrepreneurial university has become.

Universities can assess the attributes of spin-offs in terms of: 1. the science and engineering base; 2. quality of research; 3. management’s commitment to spinoffs; and 4. the entrepreneurial culture within the university (O’Shea et al. 2007). These authors present a study of MIT, and suggest that some aspects of the case are relevant for others. They point out that spin-off firms have been the subject of scholarly inquiry in disparate frameworks; viz., an institutional view seeking explanation through groups norms and culture; a resource-based school; a socio-economic view; and study at the level of individual academic entrepreneurs. Analysis of the characteristics and outcomes of spin-offs can hardly be generalized; it must be case-specific.

Spectrum of entrepreneurial activity

The traditional linear notion of the innovation system conceives of the university as simply a repository of knowledge. The usual indicators of commercialization are spin-offs, licensing, and patents. By contrast, Philpott et al. (2011) describe the EU to comprise a spectrum of activities from the “hard” (i.e., less conventionally academic) to the “soft”. See Fig.1.

As the entrepreneurial phenomenon has evolved to a complex, interactive model (Bramwell & Wolfe, 2008), knowledge creation and technology transfer is seen to occur through a variety of interactions and networks.
See Fig. 2.

facilitates the transfer of tacit knowledge between students and local and non-local ICT firms

Challenges to university administrations: complex interactive activity for research

These functions are understood theoretically: “the adoption and diffusion of new knowledge by firms involves the transfer of both codified and tacit knowledge through a process of interactive and social learning.” In other words, firms themselves must participate in this grand project of learning and innovation by cooperating with universities, suppliers and customers.

“Intermediaries” are those who fill the crucial role of connecting the creators and end-users of knowledge. Since knowledge transfers are “mainly person-embodied”, intermediaries are individuals, but also can be “independent organizations, or functions within organizations” that operate at different scales. Universities themselves establish a base of activity that builds upon itself – “a virtuous cycle that underpins economic competitiveness” (Bramwell & Wolfe, 2008).

The authors’ pre-eminent example is the University of Waterloo, which shows “a multifaceted capacity for knowledge transfer to the local economy that supports local networks and flows of knowledge, and links them with global one.” Global connections, for example, can take many forms, such as “bilateral ties between individuals in related departments to complex multidisciplinary networks, twinning arrangements and institutional consortia” (Bramwell & Wolfe, 2008: 1183). Thus the authors illustrate through the Waterloo example the variety of knowledge creation and transfer mechanisms possible for the EU at the individual and organizational levels.

These descriptions underscore current challenges to innovation and research administrations. The reality is that building a research culture is an ad hoc affair needing careful stewardship, where many aspects are difficult to institutionalize, measure, and manage. In the next post, implementation and critique of the entrepreneurial university.

Here is a continuation of academic findings on the evolution of university research and innovation. What is interesting is a move towards recognizing the significance of many aspects that are hard to organize and control.

Recommendations for university innovation policy at a general level by Smits, Kuhlmann and Teubal (2010) are that programs must now favour evolution and variation; universities must build platforms for learning and experimentation; they must stimulate demand articulation and the development of vision among business practitioners; and assist them with strategic intelligence, including environmental scan.

Significance of Complex and Varied Interactions for University Innovation

Apart from broad policy discussion, attention is also paid to the richness and variety of activity at the small firm and individual levels.

McAdam et al (2005) explain that complex and dynamic behaviour associated with technology transfer business processes, combined with the technological risk involved in the participating small firms, has led to a lack of business process definition and improvement in this area. Key research questions suggested by these authors include: is there a method for evaluating technological risk in emerging technologies within new technology based firms in university innovation centres?

These authors define a series of activities associated with technology transfer relating to such firms in university innovation centres, within a science park infrastructure:

idea generation;

new knowledge creation;

spin out and spin in companies;

technology licensing;

securing intellectual property;

venture capital and funding;

technology appraisal; and

developing business plans and business growth.

Since these activities are often complex and interrelated, and incur high risk, there is a need for: 1. systematic provision of services in relation to business and management; and 2. action research to explore the practical application of assistance techniques.

Bercovitz and Feldman (2007) point out that the university is typically thought to facilitate start-ups at the early stages of knowledge creation. “Yet, in practice, university research involves a rich mix of scientific discovery, clinical trials, beta testing, and prototype development.”

Langford et al. (2005) identify 5 pathways by which innovation crosses institutional boundaries, saying there is little Canadian information on the “subtle pathways of information exchange and technical assistance”. Innovation is “idiosyncratic, entirely dependent on context, individual and organizational capacities, and unique circumstances”. The authors call for case studies to explore these aspects.

Academic literature on innovation in the university, including research funding and R&D policy, identifies programs common in industrialized countries: spin-offs; programs to help small-medium sized enterprises access and absorb technologies; and seed and venture capital instruments. [Innovation and university references pdf.]

There is a general recognition that the conventional “linear” model has been replaced by a “holistic” or interactive model incorporating non-traditional disciplines and policy domains. Inter-disciplinary collaboration and a blurring of lines between pure and applied research has resulted in both evaluation problems and the clashing of cultures among academic fields. Governance of the innovation system is compartmentalized; policy makers face the difficulty of coordinating different societal and economic goals of research. Proponents of the controverisal entrepreneurial university (subject of a future post) construe the university’s mission as contributing to economic benefit of both the community and university faculty.

1. Significant gains in productivity were achieved not by high-tech firms themselves, but rather by the diffusion of information-communication technologies and its adoption among relatively low-tech sectors (such as retailers);

2. The part that new-intellectual-property firms play in the overall economy in terms of number of companies and revenues is very small, even if qualitatively important. It is instead other firms, second movers with different skill sets, who seize and profitably scale-up new technologies – while university licensing offices do not break even, given their research expenses;

3. The overwhelming contribution to productivity (studied in all sectors in all OECD countries) is not made by new entrants, who exit at the rate of 50-70% within 5 years, but rather by persistent firms: post-entry growth is more critical than entry per se;

4. In both the UK and the US, with similar results for the EU and Australia, it is the firm’s internal knowledge, customers, suppliers, and a list of other factors, that rank ahead of government and private research institutes as direct sources of innovation knowledge (measured both in frequency of consultation and information value).

Hughes goes on to analyze a diverse array of activities performed by universities, and reports on the perceptions by business of their relative importance for innovation. All this has relevance for nuanced decision-making in university research policy. The university, as a source of innovation and productivity, must consider itself part of a wider complex knowledge system.

In this series, I report on university research policy for innovation.

University research innovation and research funding – as a matter of economics

What is the orientation of innovation in universities? From one side, it is construed as economics. According to the Canadian Federal Government, innovation is an important key to strive towards an improved economy. In 2009 the Science and Technology Minister of State announced $5 billion investments in S&T, and explained the coordination of the initiative with tax policy, as well as the identification of themes where Canada can excel, such as health care. Innovation is an aspect of economic planning; the necessity of capturing global opportunities and improving Canada’s poor standing among the OECD countries is often cited.

The core dilemma is often characterized as how to move Canada towards greater productivity and competitiveness, and how to form a strong knowledge economy, rather than relying on the traditional resource-based economy. Contributing problems include the demographics of an aging population; poor international standing in PhD graduation rates; the private sector’s disinterest in R&D investment; many firms’ poor orientation towards innovation; brain drain; and competition from emerging global markets.

A complete and critical analysis of Canada’s innovation strategy, and its ultimate implications for Canadian society and standard of living, would have to take into account competing visions for Canada’s economy; the responsibilities of international finance and banking; global geo-politics; patterns of foreign ownership; international trade arrangements; and immigration. What is usually missing from the mainstream discourse is a critique of de-industrialization of western economies. Michael Hudson’s work is instructive.

University successes are celebrated in having established research activity, through federal funding agencies, across disciplines and through technology clusters. The fragmentation of research funding infrastructure and lack of consistency in and levels of government support have been the target of criticism. Despite improvements in research infrastructure in recent years, the resource capacity is collectively challenged to create, for example, a science policy based on a consistent and comprehensive interpretation of science issues.

Does anyone know where ‘technology assessment’, as a critical analysis of the relationship between technology and society, now stands in Canada? It seems to be construed only as “health technology assessment”. At the National Research Council web site, TA means “testing and validation” in a purely technical sense. Perhaps it has fallen to the researchers in “social innovation” to continue to throw critical light on our scientific and technological choices.

An integrated model of university innovation – social innovation

Humanities and social sciences research on many campuses is often supported in an integrated model, where diverse fields such as technology, design, geographic analysis, and community development collaborate. Social values; ethical ends; as well as fuller public participation in the process of science agenda-setting, and risk assessment, receive some specific attention. But there is a risk of an exacerbated cultural divide in the research community along the lines of natural science (“big science”) as opposed to social concerns.

Recommendations in the mainstream discourse to improve innovation fall roughly into three categories:

A. Government and private sector financial action

1. encourage the private sector to increase investment levels in R&D and to raise risk capital through tax relief, flow-through shares and government co-investment;
2. invigorate management talent in financial institutions and build capital pools to resume investment in Canadian firms;
3. revise the criteria and administration of existing tax-based incentives;

B. University action

4. re-examine universities’ patenting and knowledge flow policies;
5. promote higher education and improve graduate-level outcomes;
6. improve the mechanisms of interaction and collaboration between university research centres, private sector firms and government; grad students and new scholars can function as ‘knowledge transfer specialists’;

C. Firm action

7. embrace innovation as part of the business strategy;
8. create a drive to build export capacity;
9. small-medium size enterprises especially should take up information & communication technologies; better machinery and equipment; and focus on the development of knowledge industries.